EKF2: remove sparse vector optimization #22164
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The sparse vector template requires to know which states are non-zero in the observation jacobian. This complicates the modularity of the code when the state vector or the derivation is changed. The computation cost difference is almost negligible for this size.
dagar
approved these changes
Oct 2, 2023
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Solved Problem
The sparse vector template requires to know which states are non-zero in the observation jacobian. This complicates the modularity of the code when the state vector or the derivation is changed. The risk of forgetting to update the list of non-zero states is also high and since it's only used for a couple of matrix-vector multiplications, the reduced CPU load is very minimal (on fmu-v5, removing the optimization on the mag 3D fusion doesn't show any change in CPU usage).
Solution
Use dense vectors. The computation cost difference is almost negligible for this size.
Changelog Entry
For release notes:
Alternatives
I tried to directly compute the Kalman gain in the SymForce derivation. This could make the code faster but increases flash space. Since we're short on flash space but not on CPU usage, this option was rejected.
Test coverage
Unit tests